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chinese-novels-ner

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electra
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  1. README.md +80 -66
  2. model.safetensors +1 -1
README.md CHANGED
@@ -5,24 +5,24 @@ base_model: hfl/chinese-electra-180g-base-discriminator
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  tags:
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  - generated_from_trainer
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  model-index:
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- - name: chinese-electra-180g-base-discriminator-pro-ner-loadbest
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  results: []
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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  should probably proofread and complete it, then remove this comment. -->
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- # chinese-electra-180g-base-discriminator-pro-ner-loadbest
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  This model is a fine-tuned version of [hfl/chinese-electra-180g-base-discriminator](https://huggingface.co/hfl/chinese-electra-180g-base-discriminator) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0946
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- - Overall Precision: 0.7938
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- - Overall Recall: 0.8462
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- - Overall F1: 0.8192
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- - Overall Accuracy: 0.9687
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- - Ucm: 0.7342
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- - Lcm: 0.7019
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  ## Model description
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@@ -54,63 +54,77 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | Ucm | Lcm |
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  |:-------------:|:------:|:-----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:------:|:------:|
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- | 0.1585 | 0.0522 | 1000 | 0.2058 | 0.6663 | 0.6581 | 0.6622 | 0.9452 | 0.6323 | 0.5716 |
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- | 0.1254 | 0.1043 | 2000 | 0.1662 | 0.6425 | 0.7662 | 0.6989 | 0.9516 | 0.6342 | 0.5865 |
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- | 0.1049 | 0.1565 | 3000 | 0.1456 | 0.6853 | 0.7832 | 0.7310 | 0.9554 | 0.6639 | 0.6168 |
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- | 0.2059 | 0.2086 | 4000 | 0.1301 | 0.7379 | 0.7909 | 0.7635 | 0.9602 | 0.6845 | 0.6406 |
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- | 0.1606 | 0.2608 | 5000 | 0.1293 | 0.7245 | 0.8025 | 0.7615 | 0.9600 | 0.6865 | 0.6381 |
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- | 0.1844 | 0.3129 | 6000 | 0.1229 | 0.7254 | 0.8152 | 0.7677 | 0.9599 | 0.6839 | 0.6374 |
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- | 0.0755 | 0.3651 | 7000 | 0.1209 | 0.7417 | 0.7865 | 0.7635 | 0.9611 | 0.6935 | 0.6548 |
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- | 0.0703 | 0.4172 | 8000 | 0.1156 | 0.7530 | 0.8185 | 0.7844 | 0.9624 | 0.7110 | 0.6671 |
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- | 0.1037 | 0.4694 | 9000 | 0.1183 | 0.7465 | 0.8192 | 0.7812 | 0.9609 | 0.7097 | 0.6626 |
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- | 0.0483 | 0.5215 | 10000 | 0.1104 | 0.7856 | 0.8202 | 0.8025 | 0.9651 | 0.7245 | 0.6890 |
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- | 0.0579 | 0.5737 | 11000 | 0.1101 | 0.7619 | 0.8306 | 0.7948 | 0.9640 | 0.7116 | 0.6774 |
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- | 0.1037 | 0.6258 | 12000 | 0.1077 | 0.7667 | 0.8122 | 0.7888 | 0.9656 | 0.7226 | 0.6865 |
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- | 0.0122 | 0.6780 | 13000 | 0.1031 | 0.7584 | 0.8326 | 0.7938 | 0.9654 | 0.7161 | 0.6755 |
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- | 0.0358 | 0.7302 | 14000 | 0.0990 | 0.7827 | 0.8292 | 0.8053 | 0.9674 | 0.7303 | 0.6923 |
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- | 0.1096 | 0.7823 | 15000 | 0.0983 | 0.7759 | 0.8302 | 0.8021 | 0.9670 | 0.7290 | 0.6890 |
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- | 0.0644 | 0.8345 | 16000 | 0.0970 | 0.7755 | 0.8319 | 0.8027 | 0.9670 | 0.7297 | 0.6903 |
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- | 0.0628 | 0.8866 | 17000 | 0.0973 | 0.7779 | 0.8366 | 0.8062 | 0.9670 | 0.7290 | 0.6890 |
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- | 0.056 | 0.9388 | 18000 | 0.0965 | 0.7833 | 0.8309 | 0.8064 | 0.9676 | 0.7284 | 0.6916 |
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- | 0.0579 | 0.9909 | 19000 | 0.0951 | 0.7847 | 0.8366 | 0.8098 | 0.9678 | 0.7290 | 0.6923 |
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- | 0.0178 | 1.0431 | 20000 | 0.0971 | 0.7864 | 0.8422 | 0.8133 | 0.9675 | 0.7303 | 0.6968 |
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- | 0.089 | 1.0952 | 21000 | 0.0966 | 0.7807 | 0.8429 | 0.8106 | 0.9671 | 0.7297 | 0.6948 |
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- | 0.0103 | 1.1474 | 22000 | 0.0956 | 0.7891 | 0.8389 | 0.8133 | 0.9682 | 0.7297 | 0.6968 |
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- | 0.0855 | 1.1995 | 23000 | 0.0948 | 0.7982 | 0.8429 | 0.8199 | 0.9688 | 0.7406 | 0.7065 |
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- | 0.0841 | 1.2517 | 24000 | 0.0950 | 0.7982 | 0.8472 | 0.8220 | 0.9687 | 0.7413 | 0.7090 |
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- | 0.1833 | 1.3038 | 25000 | 0.0954 | 0.7969 | 0.8506 | 0.8228 | 0.9687 | 0.74 | 0.7077 |
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- | 0.0602 | 1.3560 | 26000 | 0.0952 | 0.7963 | 0.8476 | 0.8211 | 0.9687 | 0.7381 | 0.7039 |
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- | 0.0203 | 1.4082 | 27000 | 0.0951 | 0.7970 | 0.8486 | 0.8220 | 0.9688 | 0.7394 | 0.7045 |
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- | 0.015 | 1.4603 | 28000 | 0.0947 | 0.7989 | 0.8466 | 0.8220 | 0.9690 | 0.7394 | 0.7045 |
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- | 0.0222 | 1.5125 | 29000 | 0.0948 | 0.7977 | 0.8486 | 0.8224 | 0.9688 | 0.7387 | 0.7039 |
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- | 0.0355 | 1.5646 | 30000 | 0.0949 | 0.7957 | 0.8486 | 0.8213 | 0.9688 | 0.7381 | 0.7039 |
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- | 0.0569 | 1.6168 | 31000 | 0.0947 | 0.7910 | 0.8456 | 0.8173 | 0.9688 | 0.7335 | 0.7 |
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- | 0.0816 | 1.6689 | 32000 | 0.0947 | 0.7944 | 0.8469 | 0.8198 | 0.9687 | 0.7335 | 0.7 |
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- | 0.0587 | 1.7211 | 33000 | 0.0947 | 0.7953 | 0.8462 | 0.8200 | 0.9688 | 0.7348 | 0.7013 |
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- | 0.2129 | 1.7732 | 34000 | 0.0945 | 0.7934 | 0.8452 | 0.8185 | 0.9687 | 0.7348 | 0.7013 |
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- | 0.0336 | 1.8254 | 35000 | 0.0946 | 0.7930 | 0.8449 | 0.8182 | 0.9687 | 0.7335 | 0.7006 |
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- | 0.2079 | 1.8775 | 36000 | 0.0946 | 0.7930 | 0.8449 | 0.8182 | 0.9687 | 0.7335 | 0.7006 |
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- | 0.1941 | 1.9297 | 37000 | 0.0945 | 0.7927 | 0.8446 | 0.8178 | 0.9687 | 0.7335 | 0.7006 |
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- | 0.0594 | 1.9819 | 38000 | 0.0946 | 0.7942 | 0.8459 | 0.8193 | 0.9687 | 0.7348 | 0.7013 |
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- | 0.3883 | 2.0340 | 39000 | 0.0947 | 0.7947 | 0.8469 | 0.8200 | 0.9687 | 0.7342 | 0.7013 |
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- | 0.0689 | 2.0862 | 40000 | 0.0946 | 0.7947 | 0.8469 | 0.8200 | 0.9687 | 0.7342 | 0.7019 |
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- | 0.0193 | 2.1383 | 41000 | 0.0946 | 0.7944 | 0.8466 | 0.8196 | 0.9688 | 0.7342 | 0.7019 |
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- | 0.058 | 2.1905 | 42000 | 0.0946 | 0.7936 | 0.8462 | 0.8190 | 0.9688 | 0.7342 | 0.7019 |
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- | 0.212 | 2.2426 | 43000 | 0.0946 | 0.7930 | 0.8459 | 0.8186 | 0.9687 | 0.7335 | 0.7013 |
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- | 0.0634 | 2.2948 | 44000 | 0.0946 | 0.7933 | 0.8462 | 0.8189 | 0.9687 | 0.7335 | 0.7013 |
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- | 0.1266 | 2.3469 | 45000 | 0.0946 | 0.7941 | 0.8462 | 0.8193 | 0.9688 | 0.7342 | 0.7019 |
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- | 0.2563 | 2.3991 | 46000 | 0.0946 | 0.7941 | 0.8462 | 0.8193 | 0.9688 | 0.7342 | 0.7019 |
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- | 0.0584 | 2.4512 | 47000 | 0.0946 | 0.7941 | 0.8462 | 0.8193 | 0.9688 | 0.7342 | 0.7019 |
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- | 0.0854 | 2.5034 | 48000 | 0.0946 | 0.7941 | 0.8462 | 0.8193 | 0.9688 | 0.7342 | 0.7019 |
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- | 0.0399 | 2.5555 | 49000 | 0.0946 | 0.7941 | 0.8462 | 0.8193 | 0.9688 | 0.7342 | 0.7019 |
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- | 0.2113 | 2.6077 | 50000 | 0.0946 | 0.7941 | 0.8462 | 0.8193 | 0.9688 | 0.7342 | 0.7019 |
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- | 0.1691 | 2.6599 | 51000 | 0.0946 | 0.7941 | 0.8462 | 0.8193 | 0.9687 | 0.7342 | 0.7019 |
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- | 0.0725 | 2.7120 | 52000 | 0.0946 | 0.7938 | 0.8462 | 0.8192 | 0.9687 | 0.7342 | 0.7019 |
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- | 0.0334 | 2.7642 | 53000 | 0.0946 | 0.7938 | 0.8462 | 0.8192 | 0.9687 | 0.7342 | 0.7019 |
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- | 0.1146 | 2.8163 | 54000 | 0.0946 | 0.7938 | 0.8462 | 0.8192 | 0.9687 | 0.7342 | 0.7019 |
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- | 0.0324 | 2.8685 | 55000 | 0.0946 | 0.7938 | 0.8462 | 0.8192 | 0.9687 | 0.7342 | 0.7019 |
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- | 0.0572 | 2.9206 | 56000 | 0.0946 | 0.7938 | 0.8462 | 0.8192 | 0.9687 | 0.7342 | 0.7019 |
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- | 0.0722 | 2.9728 | 57000 | 0.0946 | 0.7938 | 0.8462 | 0.8192 | 0.9687 | 0.7342 | 0.7019 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  tags:
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  - generated_from_trainer
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  model-index:
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+ - name: chinese-electra-180g-base-discriminator-pro-ner-final
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  results: []
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
13
  should probably proofread and complete it, then remove this comment. -->
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+ # chinese-electra-180g-base-discriminator-pro-ner-final
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  This model is a fine-tuned version of [hfl/chinese-electra-180g-base-discriminator](https://huggingface.co/hfl/chinese-electra-180g-base-discriminator) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0940
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+ - Overall Precision: 0.8057
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+ - Overall Recall: 0.8460
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+ - Overall F1: 0.8253
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+ - Overall Accuracy: 0.9698
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+ - Ucm: 0.7474
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+ - Lcm: 0.7140
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | Ucm | Lcm |
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  |:-------------:|:------:|:-----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:------:|:------:|
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+ | 0.3089 | 0.0420 | 1000 | 0.2055 | 0.6352 | 0.6655 | 0.6500 | 0.9457 | 0.6555 | 0.6075 |
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+ | 0.187 | 0.0840 | 2000 | 0.1547 | 0.6632 | 0.7649 | 0.7104 | 0.9537 | 0.6670 | 0.6200 |
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+ | 0.1208 | 0.1260 | 3000 | 0.1396 | 0.7391 | 0.7471 | 0.7431 | 0.9575 | 0.6775 | 0.6409 |
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+ | 0.0619 | 0.1680 | 4000 | 0.1323 | 0.7548 | 0.7713 | 0.7629 | 0.9614 | 0.7098 | 0.6639 |
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+ | 0.153 | 0.2100 | 5000 | 0.1231 | 0.7545 | 0.8144 | 0.7833 | 0.9629 | 0.7244 | 0.6785 |
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+ | 0.2363 | 0.2520 | 6000 | 0.1252 | 0.7424 | 0.8299 | 0.7837 | 0.9624 | 0.7182 | 0.6722 |
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+ | 0.0843 | 0.2940 | 7000 | 0.1191 | 0.7625 | 0.8046 | 0.7830 | 0.9629 | 0.7046 | 0.6733 |
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+ | 0.2406 | 0.3360 | 8000 | 0.1156 | 0.7785 | 0.8040 | 0.7911 | 0.9651 | 0.7213 | 0.6879 |
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+ | 0.1422 | 0.3780 | 9000 | 0.1156 | 0.7791 | 0.8109 | 0.7947 | 0.9647 | 0.7150 | 0.6806 |
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+ | 0.1183 | 0.4200 | 10000 | 0.1107 | 0.7634 | 0.8287 | 0.7947 | 0.9652 | 0.7223 | 0.6816 |
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+ | 0.1386 | 0.4620 | 11000 | 0.1095 | 0.7796 | 0.8293 | 0.8037 | 0.9654 | 0.7223 | 0.6806 |
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+ | 0.1275 | 0.5040 | 12000 | 0.1062 | 0.7865 | 0.8425 | 0.8135 | 0.9671 | 0.7432 | 0.7004 |
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+ | 0.1585 | 0.5460 | 13000 | 0.0997 | 0.7992 | 0.8420 | 0.8200 | 0.9688 | 0.7495 | 0.7088 |
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+ | 0.0954 | 0.5880 | 14000 | 0.0973 | 0.7997 | 0.8282 | 0.8137 | 0.9686 | 0.7422 | 0.7077 |
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+ | 0.1024 | 0.6300 | 15000 | 0.0964 | 0.8004 | 0.8414 | 0.8204 | 0.9698 | 0.7380 | 0.7035 |
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+ | 0.116 | 0.6720 | 16000 | 0.0954 | 0.7917 | 0.8368 | 0.8136 | 0.9698 | 0.7317 | 0.7025 |
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+ | 0.1147 | 0.7140 | 17000 | 0.0959 | 0.8043 | 0.8552 | 0.8290 | 0.9696 | 0.7474 | 0.7150 |
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+ | 0.0163 | 0.7560 | 18000 | 0.0953 | 0.8032 | 0.8397 | 0.8210 | 0.9700 | 0.7474 | 0.7161 |
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+ | 0.0926 | 0.7980 | 19000 | 0.0949 | 0.8079 | 0.8362 | 0.8218 | 0.9703 | 0.7463 | 0.7150 |
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+ | 0.1387 | 0.8400 | 20000 | 0.0944 | 0.7972 | 0.8448 | 0.8203 | 0.9698 | 0.7463 | 0.7150 |
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+ | 0.1732 | 0.8820 | 21000 | 0.0932 | 0.8120 | 0.8489 | 0.8300 | 0.9703 | 0.7537 | 0.7213 |
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+ | 0.1174 | 0.9240 | 22000 | 0.0930 | 0.8141 | 0.8408 | 0.8273 | 0.9703 | 0.7463 | 0.7171 |
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+ | 0.0835 | 0.9660 | 23000 | 0.0935 | 0.8033 | 0.8448 | 0.8235 | 0.9694 | 0.7516 | 0.7192 |
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+ | 0.0353 | 1.0080 | 24000 | 0.0930 | 0.8109 | 0.8454 | 0.8278 | 0.9699 | 0.7547 | 0.7213 |
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+ | 0.0628 | 1.0500 | 25000 | 0.0931 | 0.8076 | 0.8420 | 0.8244 | 0.9701 | 0.7505 | 0.7182 |
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+ | 0.0683 | 1.0920 | 26000 | 0.0935 | 0.8075 | 0.8460 | 0.8263 | 0.9699 | 0.7516 | 0.7182 |
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+ | 0.0178 | 1.1340 | 27000 | 0.0938 | 0.8054 | 0.8466 | 0.8254 | 0.9700 | 0.7495 | 0.7161 |
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+ | 0.0758 | 1.1760 | 28000 | 0.0937 | 0.8106 | 0.8460 | 0.8279 | 0.9700 | 0.7526 | 0.7182 |
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+ | 0.1645 | 1.2180 | 29000 | 0.0937 | 0.8054 | 0.8443 | 0.8244 | 0.9701 | 0.7484 | 0.7161 |
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+ | 0.109 | 1.2600 | 30000 | 0.0941 | 0.8107 | 0.8466 | 0.8282 | 0.9701 | 0.7505 | 0.7182 |
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+ | 0.057 | 1.3020 | 31000 | 0.0941 | 0.8058 | 0.8466 | 0.8257 | 0.9696 | 0.7474 | 0.7140 |
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+ | 0.0791 | 1.3440 | 32000 | 0.0940 | 0.8101 | 0.8483 | 0.8287 | 0.9699 | 0.7495 | 0.7182 |
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+ | 0.0186 | 1.3860 | 33000 | 0.0940 | 0.8087 | 0.8477 | 0.8277 | 0.9698 | 0.7495 | 0.7161 |
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+ | 0.0606 | 1.4280 | 34000 | 0.0940 | 0.8103 | 0.8471 | 0.8283 | 0.9700 | 0.7505 | 0.7171 |
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+ | 0.0183 | 1.4700 | 35000 | 0.0940 | 0.8089 | 0.8466 | 0.8273 | 0.9700 | 0.7495 | 0.7161 |
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+ | 0.0213 | 1.5120 | 36000 | 0.0940 | 0.8108 | 0.8471 | 0.8286 | 0.9700 | 0.7516 | 0.7171 |
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+ | 0.0241 | 1.5540 | 37000 | 0.0941 | 0.8098 | 0.8466 | 0.8278 | 0.9698 | 0.7505 | 0.7161 |
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+ | 0.0502 | 1.5960 | 38000 | 0.0941 | 0.8089 | 0.8466 | 0.8273 | 0.9699 | 0.7495 | 0.7161 |
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+ | 0.2536 | 1.6380 | 39000 | 0.0941 | 0.8080 | 0.8466 | 0.8268 | 0.9698 | 0.7484 | 0.7150 |
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+ | 0.021 | 1.6800 | 40000 | 0.0941 | 0.8076 | 0.8466 | 0.8266 | 0.9698 | 0.7484 | 0.7150 |
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+ | 0.2905 | 1.7220 | 41000 | 0.0941 | 0.8080 | 0.8466 | 0.8268 | 0.9698 | 0.7484 | 0.7150 |
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+ | 0.0292 | 1.7640 | 42000 | 0.0941 | 0.8080 | 0.8466 | 0.8268 | 0.9699 | 0.7495 | 0.7161 |
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+ | 0.0864 | 1.8060 | 43000 | 0.0940 | 0.8080 | 0.8466 | 0.8268 | 0.9699 | 0.7484 | 0.7150 |
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+ | 0.0233 | 1.8480 | 44000 | 0.0940 | 0.8070 | 0.8460 | 0.8260 | 0.9698 | 0.7474 | 0.7140 |
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+ | 0.0813 | 1.8900 | 45000 | 0.0940 | 0.8057 | 0.8460 | 0.8253 | 0.9698 | 0.7474 | 0.7140 |
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+ | 0.0481 | 1.9320 | 46000 | 0.0940 | 0.8057 | 0.8460 | 0.8253 | 0.9698 | 0.7474 | 0.7140 |
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+ | 0.124 | 1.9740 | 47000 | 0.0940 | 0.8057 | 0.8460 | 0.8253 | 0.9698 | 0.7474 | 0.7140 |
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+ | 0.0534 | 2.0160 | 48000 | 0.0940 | 0.8057 | 0.8460 | 0.8253 | 0.9698 | 0.7474 | 0.7140 |
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+ | 0.0647 | 2.0580 | 49000 | 0.0940 | 0.8057 | 0.8460 | 0.8253 | 0.9698 | 0.7474 | 0.7140 |
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+ | 0.104 | 2.1000 | 50000 | 0.0940 | 0.8057 | 0.8460 | 0.8253 | 0.9698 | 0.7474 | 0.7140 |
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+ | 0.0765 | 2.1420 | 51000 | 0.0940 | 0.8057 | 0.8460 | 0.8253 | 0.9698 | 0.7474 | 0.7140 |
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+ | 0.0882 | 2.1840 | 52000 | 0.0940 | 0.8057 | 0.8460 | 0.8253 | 0.9698 | 0.7474 | 0.7140 |
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+ | 0.0778 | 2.2260 | 53000 | 0.0940 | 0.8057 | 0.8460 | 0.8253 | 0.9698 | 0.7474 | 0.7140 |
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+ | 0.1016 | 2.2680 | 54000 | 0.0940 | 0.8057 | 0.8460 | 0.8253 | 0.9698 | 0.7474 | 0.7140 |
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+ | 0.0329 | 2.3101 | 55000 | 0.0940 | 0.8057 | 0.8460 | 0.8253 | 0.9698 | 0.7474 | 0.7140 |
112
+ | 0.1606 | 2.3521 | 56000 | 0.0940 | 0.8057 | 0.8460 | 0.8253 | 0.9698 | 0.7474 | 0.7140 |
113
+ | 0.1131 | 2.3941 | 57000 | 0.0940 | 0.8057 | 0.8460 | 0.8253 | 0.9698 | 0.7474 | 0.7140 |
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+ | 0.078 | 2.4361 | 58000 | 0.0940 | 0.8057 | 0.8460 | 0.8253 | 0.9698 | 0.7474 | 0.7140 |
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+ | 0.0388 | 2.4781 | 59000 | 0.0940 | 0.8057 | 0.8460 | 0.8253 | 0.9698 | 0.7474 | 0.7140 |
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+ | 0.1212 | 2.5201 | 60000 | 0.0940 | 0.8057 | 0.8460 | 0.8253 | 0.9698 | 0.7474 | 0.7140 |
117
+ | 0.0687 | 2.5621 | 61000 | 0.0940 | 0.8057 | 0.8460 | 0.8253 | 0.9698 | 0.7474 | 0.7140 |
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+ | 0.0633 | 2.6041 | 62000 | 0.0940 | 0.8057 | 0.8460 | 0.8253 | 0.9698 | 0.7474 | 0.7140 |
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+ | 0.0986 | 2.6461 | 63000 | 0.0940 | 0.8057 | 0.8460 | 0.8253 | 0.9698 | 0.7474 | 0.7140 |
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+ | 0.1169 | 2.6881 | 64000 | 0.0940 | 0.8057 | 0.8460 | 0.8253 | 0.9698 | 0.7474 | 0.7140 |
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+ | 0.0476 | 2.7301 | 65000 | 0.0940 | 0.8057 | 0.8460 | 0.8253 | 0.9698 | 0.7474 | 0.7140 |
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+ | 0.0126 | 2.7721 | 66000 | 0.0940 | 0.8057 | 0.8460 | 0.8253 | 0.9698 | 0.7474 | 0.7140 |
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+ | 0.0185 | 2.8141 | 67000 | 0.0940 | 0.8057 | 0.8460 | 0.8253 | 0.9698 | 0.7474 | 0.7140 |
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+ | 0.0491 | 2.8561 | 68000 | 0.0940 | 0.8057 | 0.8460 | 0.8253 | 0.9698 | 0.7474 | 0.7140 |
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+ | 0.0996 | 2.8981 | 69000 | 0.0940 | 0.8057 | 0.8460 | 0.8253 | 0.9698 | 0.7474 | 0.7140 |
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+ | 0.1328 | 2.9401 | 70000 | 0.0940 | 0.8057 | 0.8460 | 0.8253 | 0.9698 | 0.7474 | 0.7140 |
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+ | 0.1135 | 2.9821 | 71000 | 0.0940 | 0.8057 | 0.8460 | 0.8253 | 0.9698 | 0.7474 | 0.7140 |
128
 
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  ### Framework versions
model.safetensors CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
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- oid sha256:4b9a838e67f72799e3650f463d88fd64b217bb20ee5e90d74f9fe2c56a932adf
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  size 406870532
 
1
  version https://git-lfs.github.com/spec/v1
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+ oid sha256:eae45b36472c574bcce1426f32e86f26bb9c8ec564025c3126be3e097be6b86d
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  size 406870532